Benachir Bouchikhi

E-Nose and e-Tongue combination for improved recognition of fruit juice samples

There are many important challenges related to food security analysis by application of chemical and electrochemical sensors. One critical parameter is the development of reliable tools, capable of performing an overall sensory analysis. In these systems, as much information as possible is required in relation to smell, taste and colour. Here, we investigated the possibility of using a multisensor data fusion approach, which combines an e-Nose and an e-Tongue, adept in generating combined aroma and taste profiles. In order to shed light on this concept, classification of various Tunisian fruit juices using a low-level of abstraction data fusion technique was attempted. Five tin oxide-based Taguchi Gas Sensors were applied in the e-Nose instrument and the e-Tongue was designed using six potentiometric sensors. Four different commercial brands along with eleven fruit juice varieties were characterised using the e-Nose and the e-Tongue as individual techniques, followed by a combination of the two together. Applying Principal Component Analysis (PCA) separately on the respective e-Nose and e-Tongue data, only few distinct groups were discriminated. However, by employing the low-level of abstraction data fusion technique, very impressive findings were achieved. The Fuzzy ARTMAP neural network reached a 100% success rate in the recognition of the eleven-fruit juices. Therefore, data fusion approach can successfully merge individual data from multiple origins to draw the right conclusions that are more fruitful when compared to the original single data. Hence, this work has demonstrated that data fusion strategy used to combine e-Nose and e-Tongue signals led to a system of complementary and comprehensive information of the fruit juices which outperformed the performance of each instrument when applied separately.

Aging time and brand determination of pasteurized milk using a multisensor e-nose combined with a voltammetric e-tongue

A combined approach based on a multisensor system to get additional chemical information from liquid samples through the analysis of the solution and its headspace is illustrated and commented. In the present work, innovative analytical techniques, such as a hybrid e-nose and a voltammetric e-tongue were elaborated to differentiate between different pasteurized milk brands and for the exact recognition of their storage days through the data fusion technique of the combined system. The Principal Component Analysis (PCA) has shown an acceptable discrimination of the pasteurized milk brands on the first day of storage, when the two instruments were used independently. Contrariwise, PCA indicated that no clear storage days discrimination can be drawn when the two instruments are applied separately. Mid-level of abstraction data fusion approach has demonstrated that results obtained by the data fusion approach outperformed the classification results of the e-nose and e-tongue taken individually. Furthermore, the Support Vector Machine (SVM) supervised method was applied to the new subset and confirmed that all storage days were correctly identified. This study can be generalized to several beverage and food products where their quality is based on the perception of odor and flavor.

Detection of Adulteration in Argan Oil by Using an Electronic Nose and a Voltammetric Electronic Tongue

Adulteration detection of argan oil is one of the main aspects of its quality control. Following recent fraud scandals, it is mandatory to ensure product quality and customer protection. The aim of this study is to detect the percentages of adulteration of argan oil with sunflower oil by using the combination of a voltammetric e-tongue and an e-nose based on metal oxide semiconductor sensors and pattern recognition techniques. Data analysis is performed by three pattern recognition methods: principal component analysis (PCA), discriminant factor analysis (DFA), and support vector machines (SVMs). Excellent results were obtained in the differentiation between unadulterated and adulterated argan oil with sunflower one. To the best of our knowledge, this is the first attempt to demonstrate whether the combined e-nose and e-tongue technologies could be successfully applied to the detection of adulteration of argan oil.

Instrumental assessment of red meat origins and their storage time using electronic sensing systems

Objective and rapid electronic sensing systems for distinguishing among meat species and identifying the degree of spoilage have been developed. A metal oxide sensor-based electronic nose system consisting of six sensors is designed and used to analyze the headspace emanating from beef, goat and sheep meats stored at 4 °C. A rapid, non-destructive technique based on the electronic tongue system formed by seven working electrodes is also applied and used to analyse the fingerprint of the electrochemical compounds of the three meat samples. Data analysis is performed by two pattern recognition methods: Principal Component Analysis (PCA) and Support Vector Machines (SVMs). Discrimination and classification function analyses are performed on the response of the electronic nose and electronic tongue systems to each of the three red meats. The obtained results show that the three red meats can be distinguished and the number of days spent in cold storage can be identified.

Classification of Honey According to Geographical and Botanical Origins and Detection of Its Adulteration Using Voltammetric Electronic Tongue

Main possible honey fraud is the addition of various sugar syrups. But, there are also other types of fraud, such as deception on the geographical and/or botanical origin product. Providing a product of the hive with full authenticity is therefore crucial for the preservation of beekeeping. In this pursuit, voltammetric electronic tongue (VE-tongue) was employed to classify honey samples from different geographical and botanical origins. Furthermore, VE-tongue was used to detect adulterants such as glucose syrup (GS) and saccharose syrup (SS) in honey. The data obtained were analyzed by three-pattern recognition techniques: principal component analysis (PCA), support vector machines (SVMs), and hierarchical cluster analysis (HCA). These methods enabled the classification of 18 honeys of different geographical origins and 7 honeys of different botanical origins. Excellent results were obtained also in the detection of adulterated honey. Therefore, this simple method based on VE-tongue could be useful in the honey packaging and commercialization industry.

Novel strategy for sulfapyridine detection using a fully integrated electrochemical Bio-MEMS: Application to honey analysis.

Sulfapyridine (SPy) is a sulfonamide antibiotic largely employed as veterinary drugs for prophylactic and therapeutic purposes. Therefore, its spread in the food products has to be restricted. Herein, we report the synthesis and characterization of a novel electrochemical biosensor based on gold microelectrodes modified with a new structure of magnetic nanoparticles (MNPs) coated with poly(pyrrole-co-pyrrole-2-carboxylic acid) (Py/Py-COOH) for high efficient detection of SPy. This analyte was quantified through a competitive detection procedure with 5-[4-(amino)phenylsulfonamide]-5-oxopentanoic acid-BSA (SA2-BSA) antigens toward polyclonal antibody (Ab-155). Initially, gold working electrodes (WEs) of integrated biomicro electro-mechanical system (BioMEMS) were functionalized by Ppy-COOH/MNPs, using a chronoamperometric (CA) electrodeposition. Afterward, SA2-BSA was covalently bonded to Py/Py-COOH/MNP modified gold WEs through amide bonding. The competitive detection of the analyte was made by a mixture of a fixed concentration of Ab-155 and decreasing concentrations of SPy from 50µgL-1 to 2ngL-1. Atomic Force Microscopy characterization was performed in order to ensure Ppy-COOH/MNPs electrodeposition on the microelectrode surfaces. Electrochemical measurements of SPy detection were carried out using electrochemical impedance spectroscopy (EIS). This biosensor was found to be highly sensitive and specific for SPy, with a limit of detection of 0.4ngL-1. This technique was exploited to detect SPy in honey samples by using the standard addition method. The measurements were highly reproducible for detection and interferences namely, sulfadiazine (SDz), sulfathiazole (STz) and sulfamerazine (SMz). Taking these advantages of sensitivity, specificity, and low cost, our system provides a new horizon for development of advanced immunoassays in industrial food control.

Application of a portable electronic nose device to discriminate and identify cheeses with known percentages of cow's and goat's milk

A portable electronic nose comprising an array of 6 metal oxide semiconductor sensors was developed and used, jointly with pattern recognition methods, to discriminate and identify several cheeses made from goats, cows milk and their mixtures. Principal Component Analysis (PCA) was used to visualize the different categories of aroma profiles and Multivariate Analysis of Variance (MANOVA) was performed to test the significance of the differences between cheeses groups. Database was then elaborated using supervised classifiers such as Discriminant Factor Analysis (DFA) with leave one out approach. The results indicate that the portable electronic nose device can clearly and rapidly distinguish between cows milk cheese, goats milk cheese and cheeses containing variable amounts of cows and goats milk. So, this system can be used in order to avoid fraud and to fulfill customer expectations.

Detection of seasonal allergic rhinitis from exhaled breath VOCs using an electronic nose based on an array of chemical sensors

In this study, we investigate for the first time the ability of an electronic nose (E-nose) based on an array of chemical sensors to discriminate between breath Volatile Organic Compounds (VOCs) that characterize patients with Seasonal Allergic Rhinitis (SAR) and healthy states. To reach this aim, multivariate analysis including Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA) and Support Vector Machines (SVMs) were applied for database as an alternative tools for the resolution of complex classification situations. The preliminary results reveal that VOC-patterns of exhaled breath were accurately discriminated patients with SAR from healthy controls. These findings indicate that the E-nose may succeed as a non-invasive diagnostic tool, low cost and rapid technique for breath analysis.

Emerging approach for analytical characterization and geographical classification of Moroccan and French honeys by means of a voltammetric electronic tongue

Moroccan and French honeys from different geographical areas were classified and characterized by applying a voltammetric electronic tongue (VE-tongue) coupled to analytical methods. The studied parameters include color intensity, free lactonic and total acidity, proteins, phenols, hydroxymethylfurfural content (HMF), sucrose, reducing and total sugars. The geographical classification of different honeys was developed through three-pattern recognition techniques: principal component analysis (PCA), support vector machines (SVMs) and hierarchical cluster analysis (HCA). Honey characterization was achieved by partial least squares modeling (PLS). All the PLS models developed were able to accurately estimate the correct values of the parameters analyzed using as input the voltammetric experimental data (i.e. r>0.9). This confirms the potential ability of the VE-tongue for performing a rapid characterization of honeys via PLS in which an uncomplicated, cost-effective sample preparation process that does not require the use of additional chemicals is implemented.

Data Fusion from Voltammetric and Potentiometric Sensors to Build a Hybrid Electronic Tongue Applied in Classification of Beers

A hybrid electronic tongue based on data fusion of two different sensor families was built and used to recognize three types of beer. The employed sensor array was formed by three modified graphite‐epoxy voltammetric sensors plus six potentiometric sensors with cross‐sensitivity. The sensors array coupled with feature extraction and pattern recognition methods, namely Principal Component Analysis (PCA) and Discriminant Factor Analysis (DFA), were trained to classify the data clusters related to different beer types. PCA was used to visualize the different categories of taste profiles and DFA with leave‐one‐out cross validation approach permitted the qualitative classification. According to the DFA model, 96% of beer samples were correctly classified. The aim of this work is to prove performance of hybrid electronic tongue systems by exploiting the new approach of data fusion of different sensor families, in comparison of electronic tongue with only one sensor type.